AIMS Medical Science, 2016, 3(2): 217-233. doi: 10.3934/medsci.2016.2.217.

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A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression

Centre for Research in Complex Systems and School of Community Health, Charles Sturt University, Albury, NSW, Australia

Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.
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Keywords automated detection; cardiac autonomic neuropathy; data mining; machine learning; diabetes complications

Citation: Herbert F. Jelinek, Andrei V. Kelarev. A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression. AIMS Medical Science, 2016, 3(2): 217-233. doi: 10.3934/medsci.2016.2.217


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